论文标题
在高野火点火风险下，电力线关闭和恢复的合作式化
Co-optimization of power line shutoff and restoration under high wildfire ignition risk
论文作者
论文摘要
电力基础设施点燃了最近历史上最具破坏性的野火。先发制人关闭是减轻电源线点火风险的有效工具，但同时可能会导致广泛的停电。这项工作提出了一个数学优化问题，以帮助公用事业确定何时何地实施这些关闭，以及一旦野火风险较低，如何最有效地恢复功率。具体而言，我们的模型将功率关闭（考虑到野火风险降低和停电）以及与线路检查和通电相关的限制的事后恢复工作，并将其作为滚动地平线优化问题实现，并且每当负载和Wildfire风险的新预测都可以解决时，我们的模型将实现。我们使用真正的野火风险数据和美国地质调查局的预测来证明我们在IEEE RTS-GMLC测试案例上的方法，并研究结果对预测质量，决策范围和系统修复预算的敏感性。该软件实现可在开源软件包PowerModelSwildFire.jl中获得。
Electric power infrastructure has ignited several of the most destructive wildfires in recent history. Preemptive power shutoffs are an effective tool to mitigate the risk of ignitions from power lines, but at the same time can cause widespread power outages. This work proposes a mathematical optimization problem to help utilities decide where and when to implement these shutoffs, as well as how to most efficiently restore power once the wildfire risk is lower. Specifically, our model co-optimizes the power shutoff (considering both wildfire risk reduction and power outages) as well as the post-event restoration efforts given constraints related to inspection and energization of lines, and is implemented as a rolling horizon optimization problem that is resolved whenever new forecasts of load and wildfire risk become available. We demonstrate our method on the IEEE RTS-GMLC test case using real wildfire risk data and forecasts from US Geological Survey, and investigate the sensitivity of the results to the forecast quality, decision horizon and system restoration budget. The software implementation is available in the open source software package PowerModelsWildfire.jl.
